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Automated Drug-Related Information Extraction from French Clinical Documents: ReLyfe Approach

arXiv 2112.11439 ↗First posted 29 Nov 2021cs.CL cs.AI cs.IRcited by 0

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Abstract (arXiv)

Structuring medical data in France remains a challenge mainly because of the lack of medical data due to privacy concerns and the lack of methods and approaches on processing the French language. One of these challenges is structuring drug-related information in French clinical documents. To our knowledge, over the last decade, there are less than five relevant papers that study French prescriptions. This paper proposes a new approach for extracting drug-related information from French clinical scanned documents while preserving patients' privacy. In addition, we deployed our method in a health data management platform where it is used to structure drug medical data and help patients organize their drug schedules. It can be implemented on any web or mobile platform. This work closes the gap between theoretical and practical work by creating an application adapted to real production problems. It is a combination of a rule-based phase and a Deep Learning approach. Finally, numerical results show the outperformance and relevance of the proposed methodology.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 19 references that are in the map. First posted on arXiv 29 Nov 2021.

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References · 4

  1. 2016Neural Architectures for Named Entity Recognitioncited by 550
  2. 2019Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Reviewcited by 36
  3. 2019Terminologies augmented recurrent neural network model for clinical named entity recognitioncited by 2
  4. 2016A New Data Representation Based on Training Data Characteristics to Extract Drug Named-Entity in Medical Textcited by 1

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